Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran

Table 2 Effect of CDCC’s policies on risk of COVID-19 using Bayes network model

Model	Policies and components in the Bayes network model	Prior Probability	Risk of infection (%)	Risk of infection (%)^a	Risk of infection (%)^b	Risk of infection (%)^c
First	Variable 1: observing social distancing	0.779	42.06	38.85	35.63	32.81
First	Variable 2: limiting gatherings	0.35	42.06	38.85	35.63	32.81
Second	Variable 1: personal hygiene	0.834	31.65	27.19	23.00	22.11
	Variable 2: wearing a mask	0.783
	Variable3: vaccination	0.346
Third	Variable 1: travel restriction	0.6	31.85	30.47	29.24	28.12
Third	Variable 2: job closure	0.75	31.85	30.47	29.24	28.12
Fourth	Variable 1: observing social distancing	0.779	18.87	17.52	16.17	15.22
	Variable 2: limiting gatherings	0.35
	Variable 3: personal hygiene	0.834
	Variable 4: wearing a mask	0.783
	Variable 5: vaccination	0.346
Fifth	Variable 1: observing social distancing	0.779	20.98	19.46	17.77	16.00
	Variable 2: limiting gatherings	0.35
	Variable 3: travel restriction	0.6
	Variable 4: job closure	0.75
Sixth	Variable 1: personal hygiene	0.834	16.25	15.19	14.12	13.37
	Variable 2: wearing a mask	0.783
	Variable 3: vaccination	0.346
	Variable 4: travel restriction	0.6
	Variable 5: job closure	0.75
Seventh	Variable 1: observing social distancing	0.779	17.64	16.38	15.13	14.18
	Variable 2: limiting gatherings	0.35
	Variable 3: personal hygiene	0.834
	Variable 4: wearing a mask	0.783
	Variable 5: vaccination	0.346
	Variable 6: travel restriction	0.6
	Variable 7: job closure	0.75

^aThe risk reduction of COVID-19 in the community, when the CDCC’s policies are implemented 10% more
^bThe risk reduction of COVID-19 in the community when the CDCC’s policies are implemented 20% more
^cThe risk reduction of COVID-19 in the community when the CDCC’s policies are implemented 30% more

ISSN: 1471-2458